GUEST COLUMN:
Colin Minto
Talent Acquisition Leader
Network Rail
I first started working with AI back in 2001. At the time, it was mainly about automating the sifting of large volumes of documents – a need that was familiar to both the legal and recruitment sectors.
What we were really doing was using parsing and matching technology to make sense of the mass of applications coming through. The aim was to help recruiters spot the strongest candidates more quickly and accurately by extracting and matching skills, roles and responsibilities. It wasn’t called AI then, but the principle hasn’t changed much – use technology to reduce manual effort and increase relevance.
Since then, AI’s role in recruitment and HR has expanded enormously. At Network Rail, for example, we now use inclusive language tools to improve the way we advertise roles. It’s a simple but effective application that scans job ads for potentially exclusionary language and suggests more neutral alternatives. The benefit is clear – a more inclusive and appealing job ad leads to a broader and more diverse pool of candidates.
As AI has evolved, so has our understanding of its different levels. On one end of the spectrum, we still have fairly straightforward keyword-matching systems. On the other, we now have generative AI that can process large volumes of information, learn from it, and generate useful outputs. That’s where the real opportunity lies – using AI not just to automate but to anticipate and support better decision-making.
One practical example comes from my time at a previous employer and involved setting up a system where, when a job went live, the platform would automatically match it to the best candidates already in the database. Those candidates would then receive an alert to let them know they were a potential fit. It made the whole process faster and more focused – something that is now standard in many platforms but was relatively new at the time.
We’re also using AI to create better onboarding experiences. When we were preparing to welcome a number of overseas hires from Zimbabwe, we used an AI assistant to help generate a familiarisation pack. It pulled together everything they’d need to know about moving to the UK and working at Network Rail. In the past, we might have commissioned a third party to produce something like that. But with the right prompt, we were able to create it ourselves in minutes – and it was both relevant and useful.
More recently, I’ve used AI to make sense of internal feedback. We asked our people two questions: what does it feel like to work at Network Rail, and what do you get in return? The responses were detailed and wide-ranging, so I ran them through an AI assistant to identify common themes. What came back was a clear, structured set of messages we could use on our careers site and in job adverts – all based on what our people actually said.
In terms of workforce management, one of the most valuable applications of AI is in understanding skills and matching them to future opportunities. If the system knows what training someone has done, what roles they’ve held, and what competencies they’ve demonstrated, it can help identify suitable next steps, whether that’s a promotion, a lateral move, or a development pathway. It can also flag the gaps that need to be addressed to help someone move forward. That’s a level of personalisation and foresight that would be almost impossible to achieve manually.
AI also has a clear role in analysing employee engagement data. At my previous employer, we had more than 300,000 survey responses globally. Aggregating that information and extracting meaningful themes was a huge task but one that AI can now perform almost instantly. The speed and clarity it brings to complex data sets is impressive, and it opens up possibilities that were out of reach not that long ago.
Internally at Network Rail, we’ve made AI assistants available to colleagues across the business, along with the necessary training to use them effectively. The idea is simple – equip people with tools that can support them day to day, whether they’re drafting content, solving problems, or planning development. In time, we expect these systems to recommend relevant training, job opportunities or mentoring schemes based on someone’s profile and working patterns. That’s a clear benefit to the individual, but also to the business.
Looking ahead, I’m particularly encouraged by the role AI can play in supporting skills-based hiring. By focusing on capabilities rather than traditional markers like job titles or previous employers, we can open up access to roles for people with the right potential but who might otherwise be overlooked. That feels like a step in the right direction.
There’s no doubt AI will change the shape of HR. Some tasks will be automated, some roles will evolve, and new ones will emerge. But HR has always been a resilient and adaptable profession. Like the spreadsheet didn’t eliminate accountants, AI won’t eliminate HR – it will equip it to do more, and do it better. Used thoughtfully, it’s a powerful enabler. And I believe those who embrace it now will be best placed to lead the profession into the future.
Listen to Colin discuss this and more in episode one of the DeeplearnHS podcast series, AI in Hiring & Workforce Strategy, here
